Use match config for training

src/batdetect2/plotting/evaluation.py

@@ -19,7 +19,7 @@ class ClassExamples:
     cross_triggers: List[MatchEvaluation] = field(default_factory=list)
 
 
-def plot_examples(
+def plot_example_gallery(
     matches: List[MatchEvaluation],
     preprocessor: PreprocessorProtocol,
     n_examples: int = 5,

src/batdetect2/plotting/matches.py

@@ -179,7 +179,7 @@ def plot_false_positive_match(
     plt.text(
         start_time,
         high_freq,
-        f"False Positive \nScore: {match.pred_score} \nTop Class: {match.pred_class} \nTop Class Score: {match.pred_class_score} ",
+        f"False Positive \nScore: {match.pred_score} \nTop Class: {match.pred_class} \nTop Class Score: {match.pred_class_score:.2f} ",
         va="top",
         ha="right",
         color=color,
@@ -326,7 +326,7 @@ def plot_true_positive_match(
     plt.text(
         start_time,
         high_freq,
-        f"True Positive \nClass: {match.gt_class} \nDet Score: {match.pred_score} \nTop Class Score: {match.pred_class_score} ",
+        f"True Positive \nClass: {match.gt_class} \nDet Score: {match.pred_score:.2f} \nTop Class Score: {match.pred_class_score:.2f} ",
         va="top",
         ha="right",
         color=color,
@@ -407,7 +407,7 @@ def plot_cross_trigger_match(
     plt.text(
         start_time,
         high_freq,
-        f"Cross Trigger \nTrue Class: {match.gt_class} \nPred Class: {match.pred_class} \nDet Score: {match.pred_score} \nTop Class Score: {match.pred_class_score} ",
+        f"Cross Trigger \nTrue Class: {match.gt_class} \nPred Class: {match.pred_class} \nDet Score: {match.pred_score:.2f} \nTop Class Score: {match.pred_class_score:.2f} ",
         va="top",
         ha="right",
         color=color,

src/batdetect2/train/callbacks.py

@@ -1,14 +1,21 @@
-from typing import List
+from functools import partial
+from multiprocessing import Pool
+from typing import List, Optional, Tuple
 
 from lightning import LightningModule, Trainer
 from lightning.pytorch.callbacks import Callback
 from lightning.pytorch.loggers import TensorBoardLogger
+from loguru import logger
 from soundevent import data
 from torch.utils.data import DataLoader
 
-from batdetect2.evaluate.match import match_sound_events_and_raw_predictions
+from batdetect2.evaluate.match import (
+    MatchConfig,
+    match_sound_events_and_raw_predictions,
+)
 from batdetect2.evaluate.types import MatchEvaluation, MetricsProtocol
-from batdetect2.plotting.evaluation import plot_examples
+from batdetect2.plotting.evaluation import plot_example_gallery
+from batdetect2.postprocess.types import BatDetect2Prediction
 from batdetect2.targets.types import TargetProtocol
 from batdetect2.train.dataset import LabeledDataset, TrainExample
 from batdetect2.train.lightning import TrainingModule
@@ -16,16 +23,25 @@ from batdetect2.train.types import ModelOutput
 
 
 class ValidationMetrics(Callback):
-    def __init__(self, metrics: List[MetricsProtocol], plot: bool = True):
+    def __init__(
+        self,
+        metrics: List[MetricsProtocol],
+        plot: bool = True,
+        match_config: Optional[MatchConfig] = None,
+    ):
         super().__init__()
 
         if len(metrics) == 0:
             raise ValueError("At least one metric needs to be provided")
 
-        self.matches: List[MatchEvaluation] = []
+        self.match_config = match_config
         self.metrics = metrics
         self.plot = plot
 
+        self._matches: List[
+            Tuple[data.ClipAnnotation, List[BatDetect2Prediction]]
+        ] = []
+
     def get_dataset(self, trainer: Trainer) -> LabeledDataset:
         dataloaders = trainer.val_dataloaders
         assert isinstance(dataloaders, DataLoader)
@@ -33,25 +49,33 @@ class ValidationMetrics(Callback):
         assert isinstance(dataset, LabeledDataset)
         return dataset
 
-    def plot_examples(self, pl_module: LightningModule):
+    def plot_examples(
+        self,
+        pl_module: LightningModule,
+        matches: List[MatchEvaluation],
+    ):
         if not isinstance(pl_module.logger, TensorBoardLogger):
             return
 
-        for class_name, fig in plot_examples(
+        for class_name, fig in plot_example_gallery(
-            self.matches,
+            matches,
             preprocessor=pl_module.preprocessor,
             n_examples=5,
         ):
             pl_module.logger.experiment.add_figure(
-                f"{class_name}/examples",
+                f"images/{class_name}_examples",
                 fig,
                 pl_module.global_step,
             )
 
-    def log_metrics(self, pl_module: LightningModule):
+    def log_metrics(
+        self,
+        pl_module: LightningModule,
+        matches: List[MatchEvaluation],
+    ):
         metrics = {}
         for metric in self.metrics:
-            metrics.update(metric(self.matches).items())
+            metrics.update(metric(matches).items())
 
         pl_module.log_dict(metrics)
 
@@ -60,10 +84,16 @@ class ValidationMetrics(Callback):
         trainer: Trainer,
         pl_module: LightningModule,
     ) -> None:
-        self.log_metrics(pl_module)
+        matches = _match_all_collected_examples(
+            self._matches,
+            pl_module.targets,
+            config=self.match_config,
+        )
+
+        self.log_metrics(pl_module, matches)
 
         if self.plot:
-            self.plot_examples(pl_module)
+            self.plot_examples(pl_module, matches)
 
         return super().on_validation_epoch_end(trainer, pl_module)
 
@@ -72,7 +102,7 @@ class ValidationMetrics(Callback):
         trainer: Trainer,
         pl_module: LightningModule,
     ) -> None:
-        self.matches = []
+        self._matches = []
         return super().on_validation_epoch_start(trainer, pl_module)
 
     def on_validation_batch_end(  # type: ignore
@@ -110,13 +140,26 @@ class ValidationMetrics(Callback):
         for clip_annotation, clip_predictions in zip(
             clip_annotations, raw_predictions
         ):
-            self.matches.extend(
+            self._matches.append((clip_annotation, clip_predictions))
-                match_sound_events_and_raw_predictions(
+
-                    clip_annotation=clip_annotation,
+
-                    raw_predictions=clip_predictions,
+def _match_all_collected_examples(
-                    targets=pl_module.targets,
+    pre_matches: List[Tuple[data.ClipAnnotation, List[BatDetect2Prediction]]],
-                )
+    targets: TargetProtocol,
-            )
+    config: Optional[MatchConfig] = None,
+) -> List[MatchEvaluation]:
+    logger.info("Matching all annotations and predictions")
+
+    with Pool() as p:
+        matches = p.starmap(
+            partial(
+                match_sound_events_and_raw_predictions,
+                targets=targets,
+                config=config,
+            ),
+            pre_matches,
+        )
+    return [match for clip_matches in matches for match in clip_matches]
 
 
 def _is_in_subclip(

src/batdetect2/train/config.py

@@ -4,6 +4,7 @@ from pydantic import Field
 from soundevent import data
 
 from batdetect2.configs import BaseConfig, load_config
+from batdetect2.evaluate.config import EvaluationConfig
 from batdetect2.models import BackboneConfig
 from batdetect2.postprocess import PostprocessConfig
 from batdetect2.preprocess import PreprocessingConfig
@@ -94,6 +95,7 @@ class FullTrainingConfig(BaseConfig):
         default_factory=PreprocessingConfig
     )
     postprocess: PostprocessConfig = Field(default_factory=PostprocessConfig)
+    evaluation: EvaluationConfig = Field(default_factory=EvaluationConfig)
 
 
 def load_full_training_config(

src/batdetect2/train/train.py

@@ -7,6 +7,7 @@ from loguru import logger
 from soundevent import data
 from torch.utils.data import DataLoader
 
+from batdetect2.evaluate.config import EvaluationConfig
 from batdetect2.evaluate.metrics import (
     ClassificationAccuracy,
     ClassificationMeanAveragePrecision,
@@ -82,7 +83,9 @@ def train(
     logger.info("Training complete.")
 
 
-def build_trainer_callbacks(targets: TargetProtocol) -> List[Callback]:
+def build_trainer_callbacks(
+    targets: TargetProtocol, config: EvaluationConfig
+) -> List[Callback]:
     return [
         ModelCheckpoint(
             dirpath="outputs/checkpoints",
@@ -96,7 +99,8 @@ def build_trainer_callbacks(targets: TargetProtocol) -> List[Callback]:
                     class_names=targets.class_names
                 ),
                 ClassificationAccuracy(class_names=targets.class_names),
-            ]
+            ],
+            match_config=config.match,
         ),
     ]
 
@@ -113,7 +117,7 @@ def build_trainer(
     return Trainer(
         **trainer_conf.model_dump(exclude_none=True),
         logger=build_logger(conf.train.logger),
-        callbacks=build_trainer_callbacks(targets),
+        callbacks=build_trainer_callbacks(targets, config=conf.evaluation),
     )